Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 73
Sivu ix
... Discriminant functions , 6 1.6 The selection of discriminant functions , 8 1.7 Training methods , 9 1.8 Summary of book by chapters , 11 1.9 Bibliographical and historical remarks , 12 References , 12 2 SOME IMPORTANT DISCRIMINANT ...
... Discriminant functions , 6 1.6 The selection of discriminant functions , 8 1.7 Training methods , 9 1.8 Summary of book by chapters , 11 1.9 Bibliographical and historical remarks , 12 References , 12 2 SOME IMPORTANT DISCRIMINANT ...
Sivu 7
... discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without altering the implied decision surfaces . In general , any monotonic nondecreasing function ( e.g. , logarithmic ) ...
... discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without altering the implied decision surfaces . In general , any monotonic nondecreasing function ( e.g. , logarithmic ) ...
Sivu 16
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vector Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |